Design Optimization Process Research Articles

Research of Bubble Breakup and Influencing Factors in Flotation Machine

As the core factor of flotation, bubbles have an important effect on flotation. The rotor speed, initial gas parameters and impeller structure of the flotation machine will affect the formation and movement of bubbles. It is very important to study the influence mechanism of operating parameters of flotation machine on the bubble breakup process for flotation machine design and structure optimization. This paper takes KYF-0.2m<sup>3</sup> flotation machine as the research object, establishes a single bubble analysis model, adopts the VOF (Volume of Fluid) method to analyze the influence of different initial positions of bubbles on the bubble breakup behavior, and studies the influence of impeller speed and initial position of bubbles on the bubble breakup. Result show that the breakup of bubbles mainly occurs near the stator region. With the increase of rotational speed of the impeller, the centrifugal force and the disturbance of the convection field will become greater, the time of the bubble breakup become shorter, more bubbles breakup and generate more smaller ones. With the bubble position is closer to the rotating axis of the impeller, the impact of reflow becomes stronger and the bubble breakup effect will be better, and if the bubble initial position closer to the impeller cover, the influence of impeller on the bubbles become greater and the distribution of bubbles will be more uniform.

Critical condition for initiation of dynamic recrystallisation in electron beam powder bed fused Ti-6Al-4 V Alloy

Despite the unique capabilities of Additive Manufacturing (AM) processes for producing Ti components with complex geometries, the desired properties are only achievable if a holistic scheme is devised, considering the synergistic role of post-processing steps. This work aimed to enhance the applicability of metal AM products by improving the process chain in the manufacturing industry. For this purpose, the current work demonstrates a comprehensive investigation into the hot deformation of Ti-6Al-4 V (Ti64) pre-forms produced via the Electron Beam Powder Bed Fusion (EB-PBF) process focusing on the determination of critical conditions for initiating Dynamic Recrystallization (DRX) in these components. Following a sequential evaluation procedure, hot deformation experiments were carried out at temperatures ranging from 1000 to 1200 °C and strain rates of 0.001–1 s−1 to determine the critical stress and strain required for initiating DRX in Ti64 pre-forms and compare them to their wrought counterparts. In addition, a specific Finite Element Model (FEM) was coupled with DRX kinetics equations to predict the volume percentage of DRX grains during the hot deformation of the pre-forms. The analysis of flow stress curves showed a significant peak stress at low strains, which is then followed by a period of flow softening and eventually transitions into a nearly steady-state flow at higher strains. In addition, compared to their wrought counterparts, the EB-PBF samples exhibited a significantly superior flow softening behavior, as evidenced by a more significant volume fraction of DRX and faster recrystallisation rates. It was also revealed that the normalised strain for DRX initiation in the EB-PBF Ti64 and wrought one was 0.55 and 0.67, respectively. FEM results closely matched the experimental finding, confirming its reliability in providing valuable insights into microstructural evolution and offering a time-efficient alternative for process design and property optimisation, lowering dependence on trial-and-error approaches. Through a combination of experiments, numerical analysis, and finite element simulations, this study sheds light on the macroscopic deformation and microstructural transformations occurring during hot working processes.

Multidisciplinary Design Optimization Processes for Efficiency Improvement of Aircraft: State-of-the-Art Review

Multidisciplinary Design Optimization Processes for Efficiency Improvement of Aircraft: State-of-the-Art Review

Mechanism design optimization through CAD-based Bayesian optimization and quantified constraints

This study addresses the challenge of mechanism design optimization, particularly focusing on the energy efficiency and design space of reciprocating mechanisms. The research question centers on how to effectively utilize Computer-Aided Design (CAD) simulations alongside Bayesian optimization (BO) and a constrained design space to streamline the design optimization process, overcoming the limitations of traditional kinematic and dynamic analysis methods. The objective is to investigate and develop a novel optimization framework that integrates CAD-based simulations with a BO approach. To achieve this, the study employs a methodological approach. At first, the feasibility of a chosen mechanism design is evaluated through a sequence of CAD-motion simulations to quantify the (in)feasibility of this design. When this design appears to be feasible, a CAD-based design evaluation method is started, in which the objective value is extracted by a sequence of CAD-motion simulations. In this paper, we advocate the use of non-parametric Gaussian processes to build a surrogate model of the objective function and the feasible design space constrained by static and dynamic constraints. The main research results demonstrate that the proposed CAD-based Bayesian optimization framework can effectively identify optimal design parameters that minimize the root mean square (RMS) torque while adhering to specified static and dynamic constraints. This optimization approach significantly reduces the complexity associated with analytic methods, making it scalable to more complex mechanisms and implementable by machine builders. In conclusion, the study successfully develops a novel optimization framework that leverages CAD-based simulations and Bayesian optimization to streamline the design process of mechanisms. The results of an emergency ventilator case study with three design parameters show a reduction of the RMS torque with 71% after 255 CAD-based design evaluations. Moreover, the results demonstrate the effectiveness of incorporating constraints into the design optimization process and the usage of Bayesian optimization (BO), providing insights into the obtained optimum. This approach offers probabilistic insights into the expected improvement of the optimum, highlighting that the full optimization potential is utilized in a computationally efficient manner.

Research on Output Characteristics of a Non-Contact Piezoelectric Actuator’s Micro-Displacement Amplifying Mechanism

A non-contact piezoelectric actuator is proposed. The non-contact power transfer between stator and rotor is realized by pneumatic transmission, characterized by fast response, long life, compact structure, and easy miniaturization and control. The structure of the non-contact piezoelectric actuator is designed and its working principle is elucidated. The equation of the relationship between the output displacements of the non-contact piezoelectric actuator’s micro-displacement amplifying mechanism and the input displacements of piezoelectric stack is deduced, and the simulation analysis method of output displacement of the micro-displacement amplifying mechanism is established. Using the equation and the simulation analysis, the output characteristics of micro-displacement amplifying mechanism for the non-contact piezoelectric actuator and their changes along with the system parameters are investigated. The detailed process of optimal design of the micro-displacement amplifying mechanism is given by means of mathematical statistics. The prototype is made and the performance test is carried out. The correctness of the theoretical calculation and simulation analysis is verified by comparing the experimental values with the theoretical and simulated values of the output displacement of the micro-displacement amplifying mechanism. The results show that the initial angle of bridge structure I has an obvious effect on the output characteristics of the micro-displacement amplifying mechanism in the range of 5°–15°. When the lever’s rod length is 13 mm–15 mm, the bridge structure II’s rod length is 6 mm–7 mm, and the power arm length of bridge structure I’s driving lever is 5 mm–7 mm, the bridge structure II’s rod horizontal projection length is 5 mm–6 mm and the output displacement of the micro-displacement amplifying mechanism is larger. Through the optimal design, it is obtained that the bridge structure I’s initial angle is 8°, the lever’s rod length is 15 mm, the bridge structure II’s rod length is 7 mm, and the power arm length of bridge structure I driving lever is 5 mm, the bridge structure II’s rod horizontal projection length is 6 mm, and the simulated output displacement of the micro-displacement amplifying mechanism is 0.1415 mm. The prototype test reveals that as the input excitation displacement decreases, the error increases, while as the input excitation displacement increases, the error decreases. Specifically, when the input excitation displacement is 0.005 mm, the measured output displacement of the micro-displacement amplifying mechanism is 0.1239 mm, resulting in a 19.8% deviation from the theoretical value and a 12.44% deviation from the simulated value. The research work in this paper enriches the research achievements of non-contact piezoelectric actuators, and also provides a reference for designing small structure and large travel micro-displacement amplifying mechanisms of this type of actuator.

Breakdown up to 12 Kv in NiO/β-Ga2O3 Vertical Heterojunction Rectifiers

Vertical heterojunction NiO/β n-Ga2O/n+ Ga2O3 rectifiers fabricated on 15 µm thick drift layers with carrier concentration 8.8 x1015 cm-3 and employing simple dual-layer PECVD SiNx/SiO2 edge termination demonstrate breakdown voltages (VB) up to 12 kV, on-voltage (VON) of ~2.2V and on-state resistance RON of 11.1-12 mΩ.cm2 . The breakdown field is ~8 MV. cm-1, close to the commonly accepted theoretical value. The associated power figure-of-merit, VB 2/RON is 13 GW·cm-2 . By sharp contrast, Schottky rectifiers concurrently fabricated on the same drift layers had maximum VB of 2.9 kV, but lower VON of 0.71-0.75 V. Transmission electron microscopy showed an absence of lattice damage between the PECVD and sputtered films within the device and the underlying epitaxial Ga2O3. The key advances are thicker, lower doped drift layers and optimization of edge termination design and deposition processes. Figure 1

Designing the Second Target Station with a Coupled Neutronics-Mechanical Optimization Workflow

The Second Target Station (STS) at the Spallation Neutron Source of the U.S. Department of Energy’s Oak Ridge National Laboratory is being designed to become the world’s highest peak brightness source of cold neutrons. As the STS project evolves, neutronics and other engineering analyses will inform many design iterations. To facilitate this process, a fully automated optimization workflow was developed to convert a parameterized computer-aided-design model of the target into an unstructured mesh geometry model and then to run a neutronics calculation and (optionally) a mechanical analysis for each design iteration. This workflow enables efficient, high-fidelity modeling; simulation; and optimization of new designs, as has been demonstrated for the optimization of the STS neutron moderators. In this paper, we present the results of our first major effort to automate the design optimization process for a spallation target. In the first analysis, the goal is to find optimal dimensions of a monolithic tungsten target coupled with an optimal super-Gaussian proton beam profile to deliver maximum brightness of the resulting neutron beams while maintaining good mechanical properties of the target. In the second analysis, geometric and beam parameters are optimized for an alternative design with tungsten plates, which can reach superior mechanical performance without compromising the neutronics performance.

Numerical simulations and an experimental study for optimal design of a 1500 [formula omitted] water-tube condensing boiler

The present study is focused on investigating the condensation process and numerical simulation for optimal design of a 1500 kW water-tube condensing boiler. The specific geometry of the heat exchanger of this boiler involves compact arrangement of spiral coils to enhance heat transfer efficiency and achieve high capacity. Experimental validation is conducted on a 580 kW condensing boiler. Numerical simulations of the flow for gas and water sides of the boiler are performed in parallel using the ANSYS-FLUENT software with the k-omega (SST) turbulence model. Twelve models with different combustion chamber dimensions, coil numbers and revolutions are considered. Important design criteria include flue gas temperature, combustion chamber pressure and water pressure drop in the coils. The results indicate that increasing effective heating surface area and reducing coils gap decrease flue gas temperature to the dew point. Increasing coil revolutions has a greater impact on flue gas temperature and boiler weight compared to increasing coil numbers. With the same number of coils, by adding an extra revolution the weight is increased approximately 17 % and the effective heating surface area about 21 %. On the other hand, for the same number of revolutions, each additional spiral coil increases both of the weight and the effective heating surface area approximately 4 %. Moreover, with an increase in the number of the coils and a decrease in the number of revolutions, the pressure within the combustion chamber is decreased. By reducing the gap size, the flue gas temperature is reduced approximately 2 °C and the combustion chamber pressure is increased about 18 %. Base on the design criteria, the model six with 27 spiral coils and 7–8 revolutions, is identified as the optimal design. This progress and capability allows the design and manufacture of high-capacity boilers for residential sector as well as for industrial applications.

Sample-efficient and surrogate-based design optimization of underwater vehicle hulls

Physics simulations like computational fluid dynamics (CFD) are a computational bottleneck in computer-aided design (CAD) optimization processes. To overcome this bottleneck, one requires either an optimization framework that is highly sample-efficient, or a fast data-driven proxy (surrogate model) for long-running simulations. Both approaches have benefits and limitations. Bayesian optimization is often used for sample efficiency, but it solves one specific problem and struggles with transferability; alternatively, surrogate models can offer fast and often more generalizable solutions for CFD problems, but gathering data for and training such models can be computationally demanding. In this work, we leverage recent advances in optimization and artificial intelligence (AI) to explore both of these potential approaches, in the context of designing an optimal unmanned underwater vehicle (UUV) hull. For first approach, we investigate and compare the sample efficiency and convergence behavior of different optimization techniques with a standard CFD solver in the optimization loop. For the second approach, we develop a deep neural network (DNN) based surrogate model to approximate drag forces that would otherwise be computed via the CFD solver. The surrogate model is in turn used in the optimization loop of the hull design. Our study finds that the Bayesian Optimization—Lower Condition Bound (BO-LCB) algorithm is the most sample-efficient optimization framework and has the best convergence behavior of those considered. Subsequently, we show that our DNN-based surrogate model predicts drag force on test data in tight agreement with CFD simulations, with a mean absolute percentage error (MAPE) of 1.85%. Combining these results, we demonstrate a two-orders-of-magnitude speedup (with comparable accuracy) for the design optimization process when the surrogate model is used. To our knowledge, this is the first study applying Bayesian optimization and DNN-based surrogate modeling to the problem of UUV design optimization, and we share our developments as open-source software.

Experimental, numerical simulation and genetic algorithm as a hybrid method for the design of hydrogen peroxide separation via non-dispersive solvent extraction

This work employed a hybrid approach combining experiments, numerical simulation, and genetic algorithm (GA) to optimize the non-dispersive solvent extraction (NDSX) process of H2O2. A numerical simulation model with high accuracy was developed. The relative error between the simulation results and experimental data was controlled within 2.2 %. The results indicated the velocity of lumen side and temperature are the key factors affecting the extraction efficiency. The non-dominated sorting GA-II algorithm coupled with the numerical simulation was used to optimize the NDSX process. The Pareto optimal design of the NDSX process can be achieved within the water outlet concentration range of 13.44 to 550.2 kg m-3. Intensification factors of NDSX versus extraction towers are evaluated to be 17.9 and 13.2. In summary, this work provides a design framework for NDSX of H2O2, which is expected to simultaneously promote the mass transfer efficiency and safety of the H2O2 separation process.

Mechanical Quantities Prediction of Metal Cutting by Machine Learning and Simulation Data

Metal cutting is an important process in industrial manufacturing. Using the mechanical quantities of metal cutting to optimize process design is helpful to improve productivity. However, it is expensive to obtain these quantities due to the complexity of the cutting process, including material nonlinearity, geometric nonlinearity, state nonlinearity and their interactions. In this paper, a prediction model is constructed by combining machine learning (ML) and simulation data to quickly acquire multi-difficult-to-obtain metal cutting mechanical quantities to solve this problem. First, Adaptive Smoothed Particle Hydrodynamics (ASPH) is used to generate a simulation dataset of 2000 metal cutting cases. Based on the simulation data, six machine learning (ML) methods are employed to establish two prediction models, single-task learning and multi-task learning, to predict the mechanical quantities of metal cutting. The experimental results demonstrate that the ML method can predict abundant reference data efficiently after understanding the relationship between simulation parameters and mechanical quantities from simulation data, which is expected to replace some similar and repetitive simulation work. The Multilayer Perceptron (MLP) model under the multi-task setting provides the best prediction performance, fastest prediction time efficiency, and stable model behavior. Additionally, input erasure experiments reveal that the prediction of maximum equivalent plastic strain is significantly affected by particle spacing, and cutting speed plays a vital role in predicting maximum velocity. This work highlights the promotion of the data-driven ML method in quickly obtaining abundant reference data for the metal cutting process, and provides an auxiliary means for process optimization.

Prediction and analysis of mechanical properties of hot-rolled strip steel based on an interpretable machine learning

Establishing a low-cost, high-precision predictive model for mechanical properties is crucial for enhancing the properties of steel. Due to the complexity of the steel production process, traditional mechanistic models struggle to efficiently and accurately describe the relationships among alloy elements, processes, and properties. In response to this, the paper introduces a high-precision framework for predicting and optimizing mechanical properties through feature engineering and machine learning models. Firstly, an effective dimension reduction of features is achieved through a weighted fusion heterogeneous feature selection strategy, thereby identifying the optimal model input features. Subsequently, an improved seagull optimization algorithm is utilized to optimize the hyperparameters of Extreme Gradient Boosting, further enhancing the predictive accuracy of the model. Moreover, based on the Shapley Additive Explanation method, a quantitative analysis of the model's predictive outcomes is conducted, elucidating the impacts of alloy elements and processes on mechanical properties, consistent with established principles of physical metallurgy. The proposed framework not only enables precise prediction of mechanical properties in steel but also provides theoretical guidance and technical support for process optimization design and the development of new steel grades.

Approximate modeling of cross-beam multi-axis force/moment sensor through Gaussian process and partial dependence plots for design optimization including stress topology

This study introduces a novel approximate model (AM) aimed at optimizing the design of cross-beam multi-axis force/moment sensor. It considers the characteristics of flexure hinges in elastic beams and the flexibility of beam joints, resulting in improved accuracy and broader solutions for equivalent stresses and natural frequencies compared to existing approximate models in the literature. Model validation is carried out by comparing the results with the finite element model in different cases that each case contains 1000 simulations. Validation cases combine different sensor dimensions with different forces, moments, material properties, and center hub shapes. Each validation case typically requires approximately 19 h using the finite element method (FEM) and just 1 sec with the AM. Furthermore, by using the Gaussian process and partial dependence plots, the sensitivity of the model accuracy on normalized sensor dimensions is investigated. The evaluation of model linearity and dimension normalization allows for the utilization of the novel approximate model beyond the provided dimension range, while still adhering to the normalized parameters. The implementation of AM in the design optimization problem demonstrates its suitability and advantages compared to existing literature and FEM results. When compared to finite element solutions within the sensor workspace, the approximate model exhibits a stiffness error of 5%, a strain error of 2%, a fundamental frequency error of 3%, and an equivalent stress error of 8%, while maintaining correlations of axial sensor properties above 98%.

Optimal Process Design for Wake-Up Free Hf0.5Zr0.5O2 Ferroelectric Capacitors: Toward Low-Power Devices with Enhanced Ferroelectric Performance

Ferroelectric hafnium and zirconium oxides have recently garnered significant attention due to their potential applications in in-memory computing. In this study, we present an optimized process design for a wake-up free 15 nm thick Hf0.5Zr0.5O2 (HZO) ferroelectric capacitor by fine-tuning the dual-oxygen process and incorporating oxygen annealing after post-metallization annealing (PMA). The optimized approach resulted in a competitive polarization of 28.6 μC/cm2, consistently exceeding 25 μC/cm2 at 3 V after 2 × 107 cycles, showcasing a current density of 3.2 mA/cm2 at 2 V after 105 cycles. The synergistic effect of oxygen vacancies and grain properties (grain size, phase proportion) enables competitive ferroelectric polarization at lower voltages, while the generation of WOx near the top electrode and increased grain size further ensure the reliability of the HZO ferroelectric capacitor. This work presents innovative perspectives for the development of non-volatile devices characterized by low leakage current and low power consumption.

Hydrogen–Water Isotope Catalytic Exchange Process Analysis by Simulation

The hydrogen–water isotope catalytic exchange process has been widely applied in the tritium-containing water treatment process. It can be compared and analyzed conveniently with process simulation software. In this study, the catalytic exchange process was simulated by Aspen Plus software (V11). According to the simulation results, the main reaction process was that HDO in the liquid phase converts into HD in the gas phase, and the reaction mainly occurred at the bottom of the column, exhibiting a two-orders-of-magnitude-higher reaction amount compared to that observed in the top section. Different side reactions occur at distinct positions along the column, exhibiting a reaction amount that is lower by one to two orders of magnitude compared to the main reaction and aligning in the same direction as the main reaction. The optimum operating temperature is 60~80 °C, with the best performance observed at 70 °C, because of the large reaction equilibrium constant and the suitable ratio of vapor to hydrogen (1:4~1:1.5) in the gas phase. The influence of the residence time was investigated by introducing reaction kinetic equations. The residence time should be more than 1 s to ensure an adequate reaction. The influence of operating conditions on the hydrogen–water isotope catalytic exchange process can be deeply investigated by process simulation, and more mass transfer process quantities can be obtained. It plays a promoting role in guiding the process design and condition optimization.

Sustainable process design and multi-objective optimization of efficient and energy-saving separation of xylene isomers via extractive distillation based on double extractants

The focus of the chemical industry on environmental protection and energy conservation has driven the development of green distillation technology. Xylene isomers are extensively utilized industrial raw materials, whose separation is challenging and energy-intensive due to their closely similar boiling points. Therefore, it is urgent to develop energy-saving and environmental protection technology. In this paper, an energy-saving and environmental protection process for separating the xylene isomers by extractive distillation was designed combing the vapor recompression process with feed preheating (VRAPFP). The parameters of extractive distillation processes were synthetically optimized by multi-objective genetic algorithm (MOGA) with total annual cost (TAC), CO2 emission (ECO2) and separation efficiency (Eext) as the objective evaluation indexes. The economical optimal process was used further for VRAPFP design through heat integration to reduce energy consumption, and all the designed candidate processes were synthetically analyzed, compared and evaluated with performance indexes. Results show that the VRAPFP process has superior economic efficiency and ecological sustainability compared with the existing distillation process, wherein the TAC and ECO2 are reduced by 14.2 % and 30.2 %, respectively. This work provides a new reference and idea for separating xylene isomers with the economy and developing new processes for energy conservation and environmental protection.

Optimization of Interior Engineering Design Process and Application Based on Enhancing Aesthetic Experience of Interactive Design

Optimization of Interior Engineering Design Process and Application Based on Enhancing Aesthetic Experience of Interactive Design

Optimal Design of Truss Structures for Sustainable Carbon Emission Reduction in Korean Construction

Due to the recent abnormalities in global temperature and increasing carbon emissions, the world is working to reduce carbon emissions. In particular, the construction sector accounts for about 37% of all carbon emissions, so it is one of the areas where sustainable reduction efforts must be made. Therefore, in this paper, an optimal design process was performed by evaluating carbon emissions as the objective function, a choice which differed from the objective function of the existing research used in the optimal design of truss structures. The metaheuristics algorithm used for the process was the advanced crow search algorithm. The levels of carbon emissions generated when the material of a truss structure consisted of a customary material (steel) were compared to scenarios in which timber was used, and a construction scenario centered on the Republic of Korea was established for comparison. The structures used as examples were 10-, 17-, 22-, and 120-bar truss structures. As a result, it was confirmed that truss structures using timber had fewer carbon emissions than structures using steel. In addition, it was confirmed that, even in the same timber structures, domestic timber had fewer carbon emissions than imported timber. These results confirmed that in order to achieve carbon neutrality in the construction field, carbon emissions must be considered in advance, in the design stage.

Cognitive intelligence-enabled requirements elicitation for design optimisation of smart product-service system

The rapid development of advanced technologies provides futuristic option to enhance product value with digital services, which the concept of Smart Product-Service System (SPSS) was proposed. SPSS focuses on usage data, the whole lifecycle, and the iterative optimisation. A large amount of data and context information can be collected during the usage stage, which can be used to elicit requirements to guide further design. How to realise the requirement elicitation with cognitive intelligence based on usage data is the key step for design optimisation. Therefore, this research proposes a cognitive intelligence-enabled requirements elicitation framework to help designers promote the design optimisation process. First, the digital twin-driven data collection architecture of SPSS is proposed by considering multi-sources and multi-stages. Second, the behaviour of SPSS is modelled based on Auto-Encoder which is constructed using deep learning to cognise SPSS performance. Third, the corresponding behaviour model is interpreted based on Shapley Addictive exPlanations (SHAP) to discover key factors with cognitive intelligence. Finally, the domain knowledge graph is used to generate the optimisation requirements for further design. To demonstrate the feasibility and advantages of the proposed framework, this study adopts the case study of wind turbines for illustration and verification.

Performance-based target reliability analysis of offshore wind turbine mooring lines subjected to the wind and wave

Reliability assessment is a crucial aspect of the design and operation of structures, particularly in balancing safety and cost considerations. This paper introduces a novel method for evaluating the performance-based target reliability of floating wind turbine platforms in offshore environments. The method focuses on the platform's motion modes and wave frequencies, which significantly influence the system's structural integrity and performance. An improved limit state function is proposed to enhance the accuracy of reliability calculations, specifically for steady-state conditions. The platform's six degrees of freedom motions are carefully analyzed to investigate their dependence on wave frequencies. By considering the time response of these motions and accounting for uncertainties in wave characteristics, wave impact directions, and wind effects, a comprehensive reliability analysis is conducted to assess the stability modes of the platform. This paper introduces the term 'Reliability Performance-Based' (RPB) analysis as a new concept to evaluate the system's reliability at a given performance level. Furthermore, an optimal target reliability index is defined to address the economic aspect of the design process. The proposed methodology's PEB analysis focuses on capturing uncertainties in wave characteristics and wind effects on floating wind turbine platforms. This includes a detailed examination of wave and wind-induced loads and their propagation through the system concerning its performance level. Statistical models were integrated to quantify these uncertainties, applying Monte Carlo simulations to assess their effects on the platform's reliability. This approach allows for a nuanced understanding of the interactions between environmental factors and structural responses, enhancing the precision of our reliability assessments. It enables the consideration of economic efficiency alongside safety, ensuring a balanced approach to the design and operation of the floating wind turbine platform. By providing a comprehensive reliability assessment framework, it aids in the optimization of design and decision-making processes for floating wind turbine platforms.